| Sumario: | One of the main threats that are occurring in the world is watering hole attacks, as they attack specific groups by exploiting respected sites. Legacy defenses, based on these known signatures and static rules have shown to be insufficient in the face of sophisticated attacks. This paper proposes a multi-layered defense strategy that integrates machine learning (ML) and behavioral analysis to detect and mitigate watering hole attacks. The proposed strategy involves training ML models to recognize patterns indicative of such attacks and continuously monitoring user behavior to detect anomalies. We hypothesize that this integrated approach will offer a robust and adaptive defense mechanism, enhancing the ability to detect and respond to advanced cyber threats in real-time. This paper provides a comprehensive framework for implementing this multi-layered defense strategy, contributing to the ongoing efforts to improve cybersecurity measures against watering hole attacks
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